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A novel method for machine tool structure condition monitoring based on knowledge graph

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Abstract

During a long-term structural health monitoring (SHM), large-scale machine tool structural response observed from various sensors show obvious big data characteristics. However, serious “data island” issues existing in traditional SHM inevitably limit the efficiencies of sensor data analysis. Besides, most existing identification methods for these diverse and complex signal data are manual methods, which are extremely time-consuming and inefficient. In this paper, a novel method based on knowledge graph (KG) was proposed to deal with the fine-grained domain knowledge modeling and multi-source sensor data integration problems in the field of machine tool SHM. With the help of KG-based querying and reasoning, it is more intelligent and convenient to retrieve and analyze sensor data. To verify the effectiveness of the proposed method, a long-term condition monitoring experiment was conducted on a CNC machine tool in an automobile factory. The dynamic properties of the machine tool structure were automatically identified based on KG, and a condition monitoring indicator based on the similarity of dynamic properties was applied to monitor the health condition of the machine tool. The final result showed the effectiveness of the KG for health monitoring of the machine tool structure.

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References

  1. Altintas Y, Verl A, Brecher C, Uriarte L, Pritschow G (2011) Machine tool feed drives CIRP annals 60(2):779–796

    Google Scholar 

  2. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann 59(2):717–739

    Article  Google Scholar 

  3. Möhring HC, Bertram O (2012) Integrated autonomous monitoring of ball screw drives. CIRP Ann 61(1):355–358

    Article  Google Scholar 

  4. Naha A, Samanta AK, Routray A, Deb AK (2017) Low complexity motor current signature analysis using sub-Nyquist strategy with reduced data length. IEEE Trans Instrum Meas 66(12):3249–3259

    Article  Google Scholar 

  5. Duan R, Wang F (2016) Fault diagnosis of on-load tap-changer in converter transformer based on time–frequency vibration analysis. IEEE Trans Industr Electron 63(6):3815–3823

    Article  Google Scholar 

  6. Wang Y, Xue C, Jia X, Peng X (2015) Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion. Mech Syst Signal Process 56:197–212

    Article  Google Scholar 

  7. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Industr Electron 62(6):3757–3767

    Article  Google Scholar 

  8. Prieto MD, Cirrincione G, Espinosa AG, Ortega JA, Henao H (2012) Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Trans Industr Electron 60(8):3398–3407

    Article  Google Scholar 

  9. Jin X, Sun Y, Que Z, Wang Y, Chow TW (2016) Anomaly detection and fault prognosis for bearings. IEEE Trans Instrum Meas 65(9):2046–2054

    Article  Google Scholar 

  10. Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Signal Process 38(1):165–205

    Article  Google Scholar 

  11. Kan MS, Tan AC, Mathew J (2015) A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech Syst Signal Process 62:1–20

    Article  Google Scholar 

  12. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376

    Article  Google Scholar 

  13. Reynders E, Roeck GD, Gundes Bakir P, Sauvage C (2007) Damage identification on the Tilff Bridge by vibration monitoring using optical fiber strain sensors. J Eng Mech 133(2):185–193

    Article  Google Scholar 

  14. Meruane V, Heylen W (2012) Structural damage assessment under varying temperature conditions. Struct Health Monit 11(3):345–357

    Article  Google Scholar 

  15. Ashwear N, Eriksson A (2016) Reducing effects from environmental temperature on the natural frequencies of tensegrity structures. J Sound Vib

  16. Hu WH, Thöns S, Rohrmann RG, Said S, Rücker W (2015) Vibration-based structural health monitoring of a wind turbine system Part II: environmental/operational effects on dynamic properties. Eng Struct 89:273–290

    Article  Google Scholar 

  17. Jia P, Rong Y, Huang Y (2019) Condition monitoring of the feed drive system of a machine tool based on long-term operational modal analysis. Int J Machine Tools Manufact 146:103454

  18. Luo B, Wang H, Liu H, Li B, Peng F (2018) Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Trans Industr Electron 66(1):509–518

    Article  Google Scholar 

  19. Li B, Luo B, Mao X, Cai H, Peng F, Liu H (2013) A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds. Int J Mach Tools Manuf 72:73–84

    Article  Google Scholar 

  20. Ristoski P, Paulheim H (2016) Semantic Web in data mining and knowledge discovery: a comprehensive survey. J Web Semantics 36:1–22

    Article  Google Scholar 

  21. Singhal A (2012) Introducing the knowledge graph: things, not strings. Official google blog 5:16

    Google Scholar 

  22. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data 1247–1250

  23. Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Bizer C (2015) Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web 6(2):167–195

    Article  Google Scholar 

  24. Suchanek FM, Kasneci G, Weikum G (2007) Yago: a core of semantic knowledge. In Proceedings of the 16th international conference on World Wide Web pp. 697–706

  25. Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In Twenty-Fourth AAAI conference on artificial intelligence

  26. Bordes A, Weston J, Usunier N (2014) Open question answering with weakly supervised embedding models. In Joint European conference on machine learning and knowledge discovery in databases (pp. 165–180). Springer, Berlin, Heidelberg

  27. Bordes A, Chopra S, Weston J (2014) Question answering with subgraph embeddings. arXiv preprint ar Xiv:1406.3676

  28. Li R, Mo T, Yang J, Jiang S, Li T, Liu Y (2020) Ontologies-based domain knowledge modeling and heterogeneous sensor data integration for bridge health monitoring systems. IEEE Trans Industr Inf 17(1):321–332

    Article  Google Scholar 

  29. Le-Phuoc D, Quoc HNM, Quoc HN, Nhat TT, Hauswirth M (2016) The graph of things: a step towards the live knowledge graph of connected things. J Web Semantics 37:25–35

    Article  Google Scholar 

  30. Yu T, Li J, Yu Q, Tian Y, Shun X, Xu L, Gao H (2017) Knowledge graph for TCM health preservation: design, construction, and applications. Artif Intell Med 77:48–52

    Article  Google Scholar 

  31. Zheng X, Wang B, Zhao Y, Mao S, Tang Y (2021) A knowledge graph method for hazardous chemical management: ontology design and entity identification. Neurocomputing 430:104–111

    Article  Google Scholar 

  32. Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering: principles and methods. Data Knowl Eng 25(1–2):161–197

    Article  MATH  Google Scholar 

  33. Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220

    Article  Google Scholar 

  34. Li X, Zhang S, Huang R, Huang B, Xu C, Kuang B (2018) Structured modeling of heterogeneous CAM model based on process knowledge graph. The Int J Adv Manufacturing Technol 96(9):4173–4193

    Article  Google Scholar 

  35. Li X, Zhang S, Huang R, Huang B, Xu C, Zhang Y (2018) A survey of knowledge representation methods and applications in machining process planning. The Int J Adv Manufacturing Technol 98(9):3041–3059

    Article  Google Scholar 

  36. Zhang Y, Luo X, Zhang H, Sutherland JW (2014) A knowledge representation for unit manufacturing processes. The Int J Adv Manufacturing Technol 73(5–8):1011–1031

    Article  Google Scholar 

  37. Li Z, Zhou X, Wang WM, Huang G, Tian Z, Huang S (2018) An ontology-based product design framework for manufacturability verification and knowledge reuse. The Int J Adv Manufacturing Technol 99(9):2121–2135

    Article  Google Scholar 

  38. Zhang C, Zhou G, Chang F, Yang X (2020) Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach. The Int J Adv Manufacturing Technol 106(5):2535–2551

    Article  Google Scholar 

  39. Chhun S, Moalla N, Ouzrout Y (2016) QoS ontology for service selection and reuse. J Intell Manuf 27(1):187–199

    Article  Google Scholar 

  40. Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: A Semantic Web Rule Language Combining OWL and RuleML, 2004. <http://www.w3.org/Submission/SWRL> (retrieved 12.05.04)

  41. Kemper C (2015) Querying Data in Neo4j with Cypher. Beginning Neo4j. Apress, 2015

  42. Small N (2015) The Py2neo 2.0 Handbook—Py2neo 2.0. 7 documentation.[Online]. Accessed July 10, 2015

  43. Feng GH, Pan YL (2012) Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals. Int J Mach Tools Manuf 52(1):85–96

    Article  Google Scholar 

  44. Magalhães F, Cunha Á, Caetano E, Brincker R (2010) Damping estimation using free decays and ambient vibration tests. Mech Syst Signal Process 24(5):1274–1290

    Article  Google Scholar 

  45. Wang BT, Cheng DK (2011) Modal analysis by free vibration response only for discrete and continuous systems. J Sound Vib 330(16):3913–3929

    Article  Google Scholar 

  46. Brownjohn JMW, Magalhaes F, Caetano E, Cunha A (2010) Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Eng Struct 32(8):2003–2018

    Article  Google Scholar 

  47. Kvåle KA, Øiseth O, Rönnquist A (2017) Covariance-driven stochastic subspace identification of an end-supported pontoon bridge under varying environmental conditions. In Dynamics of Civil Structures, Volume 2 (pp. 107–115). Springer, Cham

  48. Khan I, Shan D, Li Q (2016) Continuous modal parameter identification of a cable-stayed bridge based on robustious decomposition and covariance-driven stochastic subspace identification. Iranian J Sci Technol, Transac Civil Eng 40(1):11–22

    Article  Google Scholar 

  49. Reynders E, Houbrechts J, De Roeck G (2012) Fully automated (operational) modal analysis. Mech Syst Signal Process 29:228–250

    Article  Google Scholar 

  50. Choi SS, Cha SH, Tappert CC (2010) A survey of binary similarity and distance measures. Journal of systemics, cybernetics and informatics 8(1):43–48

    Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China under Grant nos. 51875224 and 51705174, major scientific and technological innovation projects in Shandong Province, 2019JZZY010442.

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chaochao Qiu, Bin Li & Songping He

  2. National NC System Engineering Research Center, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hongshan District, 1037 Luoyu Road, Wuhan, 430074, China

    Chaochao Qiu, Bin Li & Hongqi Liu

  3. School of Statistics and Mathematics Zhongnan University of Economics and Law, Wuhan, 430074, Hubei Province, China

    Caihua Hao

Authors
  1. Chaochao Qiu
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  2. Bin Li
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  3. Hongqi Liu
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  4. Songping He
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  5. Caihua Hao
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Contributions

Chaochao Qiu: writing (original draft preparation), conceptualization, methodology, software, validation. Bin Li: supervision, project administration. Hongqi Liu: supervision, writing (reviewing and editing). Songping He: supervision. Caihua Hao: data analysis.

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Correspondence to Chaochao Qiu.

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Qiu, C., Li, B., Liu, H. et al. A novel method for machine tool structure condition monitoring based on knowledge graph. Int J Adv Manuf Technol 120, 563–582 (2022). https://doi.org/10.1007/s00170-022-08757-5

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  • DOI: https://doi.org/10.1007/s00170-022-08757-5

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